The FCN head is fine-tuned for 30 epochs equaling the 20k iterations used in [1]}.
Again we use a learning rate of 0.01 with a drop to 0.001 after 15 epochs and a batch size of 64.
The design of our fully convolutional head follows [1]}: We use two convolutional layers with ReLU non-linearites.
Backbone features are fed into the second layer through a skip connection.
The resulting feature maps \(\phi \)  are then transformed to the desired output classes by a 1x1 convolution.
During training we apply 2D-dropout on \(\phi \) .
